Fix typo, update readme & minor efficiency improvement

README.md

@@ -34,17 +34,16 @@ ruby examples/xor.rb
 
 ## TODO
 
-So much. So much.
+So much.
 
+- Matrix calculations (WIP)
 - Convenience methods for setting up standard network topologies, crucially,
-  layers
-- Batch normalization/drop out/early stopping
+  layers (WIP)
+- Batch normalization/drop out/early stopping (WIP, dep. on matrix)
 - Hyperparameter optimisation
 - Other adaptive learning rate algorithms (Adadelta, Adam, etc?)
 - Explore matrix operations and other ways to optimise performance of algorithms
 - RPROP?
-- Use enumerable-statistics gem?
-- Speed up by adding a reduce step to the parallel gem?
 - More examples
 - Tests
 

lib/rann/backprop.rb

@@ -118,11 +118,11 @@ def self.run_single network, inputs, targets
       # remove this push mechanism, shouldn't be necessary and uses extra memory.
       incoming_deltas = Hash.new{ |h, k| h[k] = Hash.new{ |h, k| h[k] = [] } }
 
-      intial_timestep = inputs.size - 1
+      initial_timestep = inputs.size - 1
       connection_stack =
         network.output_neurons
           .flat_map{ |n| network.connections_to n }
-          .map{ |c| [c, intial_timestep] }
+          .map{ |c| [c, initial_timestep] }
 
       # maybe change this to traverse the static network timestep times if this
       # proves too difficult to rationalise

lib/rann/neuron.rb

@@ -5,7 +5,7 @@
 module RANN
   class Neuron
     ACTIVATION_FUNCTIONS = {
-      sig:    ->(v){ 1.to_d.div(1 + (Math::E ** -v), 10) },
+      sig:    ->(v){ 1.to_d.div(1 + Math::E.to_d.power(-v, 10), 10) },
       tanh:   ->(v){ Math.tanh(v).to_d(10) },
       relu:   ->(v){ [0.to_d, v].max },
       linear: ->(v){ v },